Senior Test Engineer

We are seeking an experienced Data Quality Engineer to join our Data Insights team, with strong expertise in ensuring data accuracy, completeness, consistency, and reliability across complex data pipelines and analytics platforms. This role focuses on designing and implementing robust data quality frameworks, automated validation checks, and testing strategies for ETL pipelines, APIs, and BI reports using SQL, Python, and modern testing tools.

You will work closely with data engineers, analysts, and business stakeholders to define data quality rules, perform impact analysis, and ensure that cloud-based data products meet functional, non-functional, regulatory, and security standards. The role combines hands-on quality engineering with responsibilities such as guiding teams on data quality best practices, overseeing test design and execution, and ensuring comprehensive documentation. Success in this role requires deep experience with data quality assurance, cloud platforms, SDLC/STLC, and a strong commitment to delivering trusted, analytics-ready data that supports informed decision-making.

Required Experience & Skills

• 610 years of relevant experience in Data Quality Engineering, including data quality assurance, ETL testing, BI/report testing within complex data ecosystems.
• Strong expertise in data quality validation, reconciliation, profiling, and anomaly detection across structured and semi-structured datasets.
• Advanced SQL proficiency, with the ability to design, write, and optimize complex queries for data validation, root-cause analysis, and reconciliation.
• Solid programming and scripting experience in Python, with exposure to test automation frameworks such as Pytest, and working knowledge of Linux/Unix environments.
• Experience implementing automated data quality checks and reusable validation frameworks using component-based or pattern-driven design.
• Hands-on experience with cloud platforms (AWS preferred), including validation of cloud-based data pipelines, storage, and analytics services.
• Experience in ETL and data pipeline testing, covering ingestion, transformation, and consumption layers.
• Strong understanding of SQL testing, ETL testing, BI/report testing, and API testing in enterprise analytics platforms.
• Experience with data profiling and report validation using BI tools such as Tableau, Superset, or equivalent visualization platforms.
• Familiarity with test management and defect tracking tools such as JIRA, X-Ray, HP ALM / Quality Center, or similar.
• Proficient in SDLC, STLC, and Agile delivery models, with experience working in cross-functional, globally distributed teams.
• Strong understanding of data quality standards, governance principles, and best practices, including accuracy, completeness, consistency, timeliness, and validity.
• Experience performing impact analysis, test estimation, test planning, and risk-based testing for data initiatives.
• Awareness of security, privacy, and compliance requirements (e.g., access control, data protection, regulated environments).
• Exposure to performance testing, data volume validation, and optimization strategies for large-scale data platforms.
• Strong analytical, problem-solving, and troubleshooting skills, with the ability to translate data issues into actionable insights.
• Excellent communication skills, a collaborative mindset, and the ability to guide teams toward quality-first engineering practices.
• Demonstrated ability to design testable, scalable, and reusable quality solutions for SaaS analytics platforms, APIs, and backend data pipelines.
• Commitment to continuous learning and staying current with Data Quality and Data Engineering trends, aligning solutions with Roches mission: Doing now what patients need next.

Key Responsibilities

• Design, implement, and maintain end-to-end data quality frameworks to ensure accuracy, completeness, consistency, and reliability of data across ingestion, transformation, and analytics layers.
• Define, document, and operationalize data quality rules, validation checks, and acceptance criteria in collaboration with business and functional stakeholders.
• Perform data profiling, reconciliation, and root-cause analysis to proactively identify data anomalies and quality risks.
• Develop and maintain automated data quality and ETL test suites using SQL, Python, and modern testing frameworks to support continuous integration and delivery.
• Validate ETL pipelines, APIs, and BI reports, ensuring alignment with functional requirements, non-functional requirements, and downstream analytics needs.
• Partner closely with Data Engineering, Analytics, and Platform teams to embed quality controls early in the data lifecycle.
• Conduct impact analysis and risk assessments for data changes, schema updates, and new data sources.
• Review and validate technical designs, ensuring solutions are testable, scalable.